Governing Trustworthy AI Outputs in a Sensor-Dense Society: Privacy, Auditability and Responsible Deployment

Dear Colleagues,

This Special Issue focuses on building privacy protection infrastructure for AI systems deployed in sensor-rich environments, including camera networks, IoT devices and edge AI in public spaces, smart mobility and smart cities. In the AI/AGI era, effective privacy protection is increasingly an infrastructure-scale socio-technical challenge that requires integrated design across technical controls, organizational workflows and regulatory compliance, grounded in normative values central to democratic societies (e.g., privacy, freedom, equality, due process, transparency and legitimacy). We welcome normative, ethical and philosophical analyses to the extent that they yield actionable implications for governance mechanisms, design requirements, or evaluation criteria.

A central theme of the Special Issue is output governance (egress control): how AI-generated external outputs (alerts, summaries, identification-like signals, or data sharing) can be purpose-bounded, minimized, verified and audited without undermining real-time utility. Given practical constraints where perfect secrecy or complete non-collection is not always feasible, output-layer governance provides a concrete point of control where societal impact materializes. We therefore encourage approaches that strengthen auditability and accountability, such as tamper-evident and append-only logs, verifiable output records, provenance/traceability, policy enforcement, transparency reporting and exception-access workflows with procedural safeguards and post-hoc review.

To stimulate constructive debate, we also invite complementary and alternative approaches, including input governance (data minimization, redaction/masking, privacy-preserving data pipelines), internal process controls (runtime safeguards, verification and controlled updates) and lifecycle methods such as machine unlearning and deletion compliance. By bridging emerging technical work on auditing, provenance and verifiable ML with institutional and regulatory perspectives, this Special Issue aims to supplement existing literature and provide a shared foundation for responsible, deployable privacy governance in sensor-rich societies.

Prof. Dr. Yusaku Fujii
Guest Editor

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• privacy governance infrastructure
• sensor-rich systems
• edge AI
• smart cities
• smart transportation
• output governance
• egress control
• purpose limitation
• least disclosure
• audit trail
• tamper-evident logging
• provenance
• traceability
• accountability
• compliance
• verifiable records
• transparency reporting
• verifiable inference
• ZKML
• machine unlearning
• exception access governance.